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Hugging Face has called on the US government to prioritise open-source development in its forthcoming AI Action Plan. The company prioritises efficient and reliable adoption of AI. The tools developed at Hugging Face, from model documentation to evaluation libraries, are directly shaped by these questions.
This year’s announcements covered everything from powerhouse GPUs to sleek open-source software, forming a two-pronged strategy that’s all about speed, scale, and smarter AI. With hardware like Blackwell Ultra and Rubin, and tools like Llama Nemotron and Dynamo, NVIDIA is rewriting what’s possible for AIdevelopment.
What happened this week in AI by Louie The ongoing race between open and closed-source AI has been a key theme of debate for some time, as has the increasing concentration of AIresearch and investment into transformer-based models such as LLMs. This would be its 5th generation AI training cluster.
(This could result from companies making attempts to prevent the above two failure modes - i.e., AIs might be penalized heavily for saying false and harmful things, and respond by simply refusing to answer lots of questions). The most straightforward way to solve these problems involves training AIs to behave more safely and helpfully.
I’ll argue that if today’s AIdevelopment methods lead directly to powerful enough AI systems, disaster is likely 1 by default (in the absence of specific countermeasures). I assume the world could develop extraordinarily powerful AI systems in the coming decades. I call this nearcasting.)
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